--- title: Image processing subtitle: Noise and filtering notebook bigimg: /pages/c_02_image_processing/c_02_bigimg.jpg share-img: https://raw.githubusercontent.com/YoniChechik/AI_is_Math/master/docs/pages/c_02_image_processing/c_02_bigimg.jpg layout: notebook --- noise_and_filtering

Noise and filtering

Open In Colab

In [1]:
# to run in google colab
import sys
if 'google.colab' in sys.modules:
    import subprocess
    subprocess.call('apt-get install subversion'.split())
    subprocess.call('svn export https://github.com/YoniChechik/AI_is_Math/trunk/c_02_image_processing/Tour_Eiffel.jpg'.split())
In [2]:
import numpy as np
import cv2
from matplotlib import pyplot as plt

figsize = (10,10)

Get basic image:

In [3]:
def plot_im(img, title):
    plt.figure(figsize=figsize)
    plt.imshow(img)
    plt.title(title)
    plt.xticks([])
    plt.yticks([])
    plt.show()

img = cv2.imread("Tour_Eiffel.jpg")
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plot_im(img, "orig")

mean filter

In [4]:
def mean_kernel_smoothing(img, sz):
    kernel = np.ones((sz, sz))/(sz**2)
    dst = cv2.filter2D(img, -1, kernel)

    plot_im(dst, str(sz)+'X'+str(sz)+" mean kernel")


mean_kernel_smoothing(img, 5)
mean_kernel_smoothing(img, 10)
mean_kernel_smoothing(img, 20)

gaussian filter

In [5]:
def gauss_blur(img, k_sz, sigma=-1, is_plot_kernel=False):
    blur = cv2.GaussianBlur(img, (k_sz, k_sz), sigma)
    plot_im(blur, "gaussian kernel with kernel_size="
            + str(k_sz)+r", $\sigma$=" + str(sigma))
    if is_plot_kernel:
        gauss_ker = cv2.getGaussianKernel(k_sz, sigma)
        plt.figure(figsize=(figsize[0]/2, figsize[1]/2))
        plt.plot(gauss_ker)
        plt.title("corresponding gaussian kernel")
        plt.show()

gauss_blur(img, 5, is_plot_kernel=True)
gauss_blur(img, 21, is_plot_kernel=True)
gauss_blur(img, 21, 1, is_plot_kernel=True)

madian filter

In [6]:
def median_blur(img, k_sz):
    res = cv2.medianBlur(img, k_sz)
    plot_im(res, "median filter with kernel_size="+str(k_sz))


median_blur(img, 5)

noise addition func

In [7]:
def noisy(noise_typ, image, gauss_var=1000, s_p_ratio=0.04):
    # modified from: https://stackoverflow.com/questions/22937589/how-to-add-noise-gaussian-salt-and-pepper-etc-to-image-in-python-with-opencv
    if noise_typ == "gauss":
        mean = 0
        sigma = gauss_var**0.5
        gauss = np.random.normal(mean, sigma, image.shape)

        res = image + gauss
        noisy = np.clip(res,0,255).astype(np.uint8)
        return noisy
    elif noise_typ == "s&p":
        # this implementation is not entirely correct because it assumes that
        # only 0 OR 255 values are S&P noise.
        out = np.copy(image)

        # Salt mode
        mask = np.random.rand(image.shape[0],image.shape[1]) <= s_p_ratio/2
        out[mask] = 255

        # Pepper mode
        mask = np.random.rand(image.shape[0],image.shape[1]) <= s_p_ratio/2
        out[mask] = 0
        return out

gaussian noise tests

In [8]:
np.random.seed(1234)

gauss_noise_im = noisy("gauss", img, gauss_var=70)
plt.figure(figsize=figsize)
plt.imshow(gauss_noise_im)
plt.title('original image + gaussian noise')
plt.show()
In [9]:
gauss_blur(gauss_noise_im, 3)
gauss_blur(gauss_noise_im, 5)
gauss_blur(gauss_noise_im, 11)
median_blur(gauss_noise_im, 5)

salt and pepper noise test

In [10]:
s_p_noise_im = noisy("s&p", img, s_p_ratio=0.04)
plt.figure(figsize=figsize)
plt.imshow(s_p_noise_im)
plt.title('original image + s&p noise')
plt.show()
In [11]:
gauss_blur(s_p_noise_im, 5)
median_blur(s_p_noise_im, 3)
median_blur(s_p_noise_im, 5)
In [ ]: